# Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP

arXiv: https://arxiv.org/abs/2411.01527 · HTML: https://mlsanity.com/paper/2411.01527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 3 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Well-tuned Simple Nets Excel on Tabular Datasets](https://mlsanity.com/paper/2106.11189/) (2021)
- [Weakly Supervised Action Localization by Sparse Temporal Pooling Network](https://mlsanity.com/paper/1712.05080/) (2017)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [Learnable Dynamic Temporal Pooling for Time Series Classification](https://mlsanity.com/paper/2104.02577/) (2021)
- [Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction](https://mlsanity.com/paper/2203.00971/) (2022)
- [Quantifying Long Range Dependence in Language and User Behavior to improve RNNs](https://mlsanity.com/paper/1905.09414/) (2019)
- [Deep Adaptive Temporal Pooling for Activity Recognition](https://mlsanity.com/paper/1808.07272/) (2018)

## Abstract (arXiv)

Environmental monitoring and predictive modeling of the Water Quality Index (WQI) through the assessment of the water quality.